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+<h1 align="center">Boltz-1:
+
+Democratizing Biomolecular Interaction Modeling
+</h1>
+
+Boltz-1 is an open-source model which predicts the 3D structure of proteins, rna, dna and small molecules; it handles modified residues, covalent ligands and glycans, as well as condition the generation on pocket residues. 
+
+For more information about the model, see our [technical report](https://gcorso.github.io/assets/boltz1.pdf).
+
+## Installation
+Install boltz with PyPI (recommended):
+
+```
+pip install boltz
+```
+
+or directly from GitHub for daily updates:
+
+```
+git clone https://github.com/jwohlwend/boltz.git
+cd boltz; pip install -e .
+```
+> Note: we recommend installing boltz in a fresh python environment
+
+## Inference
+
+You can run inference using Boltz-1 with:
+
+```
+boltz predict input_path
+```
+
+Boltz currently accepts three input formats:
+
+1. Fasta file, for most use cases
+
+2. A comprehensive YAML schema, for more complex use cases
+
+3. A directory containing files of the above formats, for batched processing
+
+To see all available options: `boltz predict --help` and for more informaton on these input formats, see our [prediction instructions](docs/prediction.md).
+
+## Training
+
+If you're interested in retraining the model, see our [training instructions](docs/training.md).
+
+## Contributing
+
+We welcome external contributions and are eager to engage with the community. Connect with us on our [Slack channel](https://boltz-community.slack.com/archives/C0818M6DWH2) to discuss advancements, share insights, and foster collaboration around Boltz-1.
+
+## Coming very soon
+
+- [ ] Pocket conditioning support
+- [ ] More examples
+- [ ] Full data processing pipeline
+- [ ] Colab notebook for inference
+- [ ] Confidence model checkpoint
+- [ ] Support for custom paired MSA
+- [ ] Kernel integration
+
+## License
+
+Our model and code are released under MIT License, and can be freely used for both academic and commercial purposes.